Scientific Discovery

First published Thu Mar 6, 2014

Scientific discovery is the process or product of successful
scientific inquiry. Objects of discovery can be things, events,
processes, causes, and properties as well as theories and hypotheses
and their features (their explanatory power, for example). Most
philosophical discussions of scientific discoveries focus on the
generation of new hypotheses that fit or explain given data sets or
allow for the derivation of testable consequences. Philosophical
discussions of scientific discovery have been intricate and complex
because the term “discovery” has been used in many
different ways, both to refer to the outcome and to the procedure of
inquiry. In the narrowest sense, the term “discovery”
refers to the purported “eureka moment” of having a new
insight. In the broadest sense, “discovery” is a synonym
for “successful scientific endeavor” tout court. Some
philosophical disputes about the nature of scientific discovery
reflect these terminological variations.

Philosophical issues related to scientific discovery arise about the
nature of human creativity, specifically about whether the
“eureka moment” can be analyzed and about whether there
are rules (algorithms, guidelines, or heuristics) according to which
such a novel insight can be brought about. Philosophical issues also
arise about rational heuristics, about the characteristics of
hypotheses worthy of articulation and testing, and, on the meta-level,
about the nature and scope of philosophical reflection itself. This
essay describes the emergence and development of the philosophical
problem of scientific discovery, surveys different philosophical
approaches to understanding scientific discovery, and presents the
meta-philosophical problems surrounding the debates.

Philosophical reflection on scientific discovery occurred in different
phases. Prior to the 1930s, philosophers were mostly concerned with
discoveries in the broadest sense of the term, that is, with the
analysis of successful scientific inquiry as a whole. Philosophical
discussions focused on the question of whether there were any
discernible patterns in the production of new knowledge. Because the
concept of discovery did not have a specified meaning and was used in
a very broad sense, almost all seventeenth- and eighteenth-century
treatises on scientific method could potentially be considered as
early contributions to reflections on scientific discovery. In the
course of the 19th century, as philosophy of science and
science became two distinct endeavors, the term
“discovery” became a technical term in philosophical
discussions. Different elements of scientific inquiry were
specified. Most importantly, the generation of new knowledge was
clearly and explicitly distinguished from its validation, and thus the
conditions for the narrower notion of discovery as the act of
conceiving new ideas emerged.

The next phase in the discussion about scientific discovery began with
the introduction of the so-called “context distinction,”
the distinction between the “context of discovery” and the
“context of justification”. It was further assumed that
the act of conceiving a new idea is a non-rational process, a leap of
insight that cannot be regulated. Justification, by contrast, is a
systematic process of applying evaluative criteria to knowledge
claims. Advocates of the context distinction argued that philosophy of
science is exclusively concerned with the context of
justification. The assumption underlying this argument is that
philosophy is a normative project; it determines norms for scientific
practice. Given these assumptions, only the justification of ideas,
not their generation, can be the subject of philosophical (normative)
analysis. Discovery, by contrast, can only be a topic for empirical
study. By definition, the study of discovery is outside the scope of
philosophy of science proper.

The introduction of the context distinction and the disciplinary
distinction that was tied to it spawned meta-philosophical disputes.
For a long time, philosophical debates about discovery were shaped by
the notion that philosophical and empirical analyses are mutually
exclusive. A number of philosophers insisted, like their predecessors
prior to the 1930s, that the philosopher's tasks include the analysis
of actual scientific practices and that scientific resources be used
to address philosophical problems. They also maintained that it is a
legitimate task for philosophy of science to develop a theory of
heuristics or problem solving. But this position was the minority view
during much of 20th-century philosophy of science.
Philosophers of discovery were thus compelled to demonstrate that
scientific discovery was in fact a legitimate part of philosophy of
science. Philosophical reflections about the nature of scientific
discovery had to be bolstered by meta-philosophical arguments about
the nature and scope of philosophy of science.

Today, however, there is wide agreement that philosophy and empirical
research are not mutually exclusive. Not only do empirical studies of
actual scientific discoveries inform philosophical thought about the
structure and cognitive mechanisms of discovery, but researches in
psychology, cognitive science, artificial intelligence and related
fields have become an integral part of philosophical analyses of the
processes and conditions of the generation of new knowledge.

Prior to the 19th century, the term “discovery”
commonly referred to the product of successful
inquiry. “Discovery” was used broadly to refer to a new
finding, such as a new cure, an improvement of an instrument, or a new
method of measuring longitude. Several natural and experimental
philosophers, notably Bacon, Descartes, and Newton, expounded accounts
of scientific methods for arriving at new knowledge. These accounts
were not explicitly labeled “methods
of discovery”, but the general accounts of scientific
methods are nevertheless relevant for current philosophical debates
about scientific discovery. They are relevant because philosophers of
science have frequently presented 17th-century theories of
scientific method as a contrast class to current philosophies of
discovery. The distinctive feature of the 17th- and
18th-century accounts of scientific method is that the
methods have probative force (Nickles 1985). This means that those
accounts of scientific method function as guides for acquiring new
knowledge and at the same time as validations of the knowledge thus
obtained (Laudan 1980; Schaffner 1993: chapter 2).

Bacon's account of his “new method” as it is presented in
the Novum Organum is a prominent example. Bacon's work showed
how best to arrive at knowledge about “form natures” (the
most general properties of matter) via a systematic investigation of
phenomenal natures. Bacon described how first to collect and organize
natural phenomena and experimental facts in tables, how to evaluate
these lists, and how to refine the initial results with the help of
further experiments. Through these steps, the investigator would
arrive at conclusions about the “form nature” that
produces particular phenomenal natures. The point is that for Bacon,
the procedures of constructing and evaluating tables and conducting
experiments according to the Novum Organum leads to secure
knowledge. The procedures thus have “probative force”.

Similarly, Newton's aim in the Philosophiae Naturalis Principia
Mathematica was to present a method for the deduction of
propositions from phenomena in such a way that those propositions
become “more secure” than propositions that are secured by
deducing testable consequences from them (Smith 2002). Newton did not
assume that this procedure would lead to absolute certainty. One could
only obtain moral certainty for the propositions thus secured. The
point for current philosophers of science is that these approaches are
generative theories of scientific method. Generative theories of
scientific method assume that propositions can only be established and
secured by showing that they follow from observed and experimentally
produced phenomena. In contrast, non-generative theories of scientific
method—such as the one proposed by Huygens—assumed that
propositions must be established by comparing their consequences with
observed and experimentally produced phenomena. In
20th-century philosophy of science, this approach is often
characterized as “consequentialist” (Laudan 1980; Nickles
1985).

Recent philosophers of science have used historical sketches like
these to construct the prehistory of current philosophical debates
about scientific discovery. The argument is that scientific discovery
became a problem for philosophy of science in the 19th
century, when consequentialist theories of scientific method became
more widespread. When consequentialist theories were on the rise, the
two processes of conception and validation of an idea or hypothesis
became distinct and the view that the merit of a new idea does not
depend on the way in which it was arrived at became widely
accepted.

In the course of the 19th century, the act of having an
insight—the purported “eureka moment”—was
separated from processes of articulating, developing, and testing the
novel insight. Philosophical discussion focused on the question of
whether and to what extent rules could be devised to guide each of
these processes. William Whewell's work, especially the two volumes
of Philosophy of the Inductive Sciences of 1840, is an
important contribution to the philosophical debates about scientific
discovery precisely because he clearly separated the creative moment
or “happy thought” as he called it from other elements of
scientific inquiry. For Whewell, discovery comprised all three
elements: the happy thought, the articulation and development of that
thought, and the testing or verification of it. In most of the
subsequent treatments of discovery, however, the scope of the term
“discovery” is limited to either the first of these
elements, the “happy thought”, or to the first two of
these elements, the happy thought and its articulation. In fact, much
of the controversies in the 20th century about the
possibility of a philosophy of discovery can be understood against the
background of the disagreement about whether the process of discovery
does or does not include the articulation and development of a novel
thought.

The previous section shows that scholars like Bacon and Newton aimed
to develop methodologies of scientific inquiry. They proposed
“new methods” or “rules of reasoning” that
guide the generation of certain propositions from observed and
experimental phenomena. Whewell, by contrast, was explicitly concerned
with developing a philosophy of discovery. His account was in part a
description of the psychological makeup of the discoverer. For
instance, he held that only geniuses could have those happy thoughts
that are essential to discovery. In part, his account was an account
of the methods by which happy thoughts are integrated into the system
of knowledge. According to Whewell, the initial step in every
discovery is what he called “some happy thought, of which we
cannot trace the origin, some fortunate cast of intellect, rising
above all rules. No maxims can be given which inevitably lead to
discovery” (Whewell 1996 [1840]: 186). An “art of
discovery” in the sense of a teachable and learnable skill does
not exist according to Whewell. The happy thought builds on the known
facts, but according to Whewell it is impossible to prescribe a method
for having happy thoughts.

In this sense, happy thoughts are accidental. But in an important
sense, scientific discoveries are not accidental. The happy
thought is not a “wild guess.” Only the person whose mind
is prepared to see things will actually notice them. The
“previous condition of the intellect, and not the single fact,
is really the main and peculiar cause of the success. The fact is
merely the occasion by which the engine of discovery is brought into
play sooner or later. It is, as I have elsewhere said, only the spark
which discharges a gun already loaded and pointed; and there is little
propriety in speaking of such an accident as the cause why the bullet
hits its mark.” (Whewell 1996 [1840]: 189).

Having a happy thought is not yet a discovery, however. The second
element of a scientific discovery consists in binding
together—“colligating”, as Whewell called it—a
set of facts by bringing them under a general conception. Not only
does the colligation produce something new, but it also shows the
previously known facts in a new light. More precisely, colligation
works from both ends, from the facts as well as from the ideas that
bind the facts together. Colligation is an extended process. It
involves, on the one hand, the specification of facts through
systematic observation, measurements and experiment, and on the other
hand, the clarification of ideas through the exposition of the
definitions and axioms that are tacitly implied in those ideas. This
process is iterative. The scientists go back and forth between binding
together the facts, clarifying the idea, rendering the facts more
exact, and so on and so forth.

The final part of the discovery is the verification of the colligation
involving the happy thought. This means, first and foremost, that the
outcome of the colligation must be sufficient to explain the data at
hand. Verification also involves judging the predictive power,
simplicity, and “consilience” of the outcome of the
colligation. “Consilience” refers to a higher range of
generality (broader applicability) of the theory (the articulated and
clarified happy thought) that the actual colligation
produced. Whewell's account of discovery is not a deductivist
system. It is essential that the outcome of the colligation be
inferable from the data prior to any testing (Snyder 1997).

Whewell's theory of discovery is significant for the philosophical
debate about scientific discovery because it clearly separates three
elements: the non-analyzable happy thought or “eureka
moment”; the process of colligation which includes the
clarification and explication of facts and ideas; and the verification
of the outcome of the colligation. His position that the philosophy of
discovery cannot prescribe how to think happy thoughts has been a key
element of 20th-century philosophical reflection on
discovery, and many philosophers have adopted the notion “happy
thought” as a label for the “eureka moment” involved
in discovery. Notably, however, Whewell's conception of discovery not
only comprises the happy thoughts but also the processes by which the
happy thoughts are to be integrated into the given system of
knowledge. The procedures of articulation and test are both
analyzable according to Whewell, and his conception of colligation and
verification serve as guidelines for how the discoverer should
proceed. A colligation, if properly done, has as such
justificatory force. Similarly, the process of verification is an
integral part of discovery and it too has justificatory force.
Whewell's conception of verification thus comprises elements of
generative and consequential methods of inquiry. To verify a
hypothesis, the investigator needs to show that it accounts for the
known facts, that it foretells new, previously unobserved phenomena,
and that it can explain and predict phenomena which are explained and
predicted by a hypothesis that was obtained through an independent
happy thought-cum-colligation (Ducasse 1951).

Whewell's conceptualization of scientific discovery offers a useful
framework for mapping the philosophical debates about discovery and
for identifying major issues of concern in recent philosophical
debates. First and foremost, nearly all recent philosophers operate
with a notion of discovery that is narrower than Whewell's. In the
narrower conception, what Whewell called “verification” is
not part of discovery proper. Secondly, until the late 20th
century, there was wide agreement that the “eureka
moment,” narrowly construed, is an unanalyzable, even mysterious
leap of insight. The main disagreements concerned the question of
whether the process of developing a hypothesis (the
“colligation” in Whewell's terms) is or is not a part of
discovery proper, and if it is, whether and how this process is guided
by rules. Philosophers also disagreed on the issue of whether it is a
philosophical task to explicate these rules. In recent decades,
philosophical attention has shifted to the “eureka
moment”. Drawing on resources from cognitive science,
neuroscience, computational research, and environmental and social
psychology, they have “demystified” the cognitive
processes involved in the generation of new ideas.

In the early 20th century, the view that discovery is or at
least crucially involves a non-analyzable creative act of a gifted
genius was widespread but not unanimously accepted. Alternative
conceptions of discovery emphasize that discovery is an extended
process, i.e., that the discovery process includes the reasoning
processes through which a new insight is articulated and further
developed. Moreover, it was assumed that there is a systematic, formal
aspect to that reasoning. While the reasoning involved does not
proceed according to the principles of demonstrative logic, it is
systematic enough to deserve the label
“logical”. Proponents of this view argued that traditional
(here: Aristotelian) logic is an inadequate model of scientific
discovery because it misrepresents the process of knowledge generation
as grossly as the notion of “happy thought”. In this
approach, the term “logic” is used in the broad sense. It
is the task of the logic of discovery to draw out and give a schematic
representation of the reasoning strategies that were applied in
episodes of successful scientific inquiry. Early
20th-century logics of discovery can best be described as
theories of the mental operations involved in knowledge
generation. Among these mental operations are classification,
determination of what is relevant to an inquiry, and the conditions of
communication of meaning. It is argued that these features of
scientific discovery are either not or insufficiently represented by
traditional logic (Schiller 1917: 236–7).

Philosophers advocating this approach agree that the logic of
discovery should be characterized as a set of heuristic principles
rather than as a process of applying inductive or deductive logic to a
set of propositions. These heuristic principles are not understood to
show the path to secure knowledge. Heuristic principles are suggestive
rather than demonstrative (Carmichael 1922, 1930). One recurrent
feature in these accounts of the reasoning strategies leading to new
ideas is analogical reasoning (Schiller 1917; Benjamin 1934). In the
20th century, it is widely acknowledged that analogical
reasoning is a productive form of reasoning that cannot be reduced to
inductive or deductive reasoning. However, these approaches to the
logic of discovery remained scattered and tentative at that time, and
attempts to develop more systematically the heuristics guiding
discovery processes were eclipsed by the advance of the distinction
between contexts of discovery and justification.

The distinction between “context of discovery” and
“context of justification” dominated and shaped the
discussions about discovery in 20th-century philosophy of
science. The context distinction marks the distinction between the
generation of a new idea or hypothesis and the defense (test,
verification) of it. As the previous sections have shown, the
distinction among different features of scientific inquiry has a
longer history, but in philosophy of science it became potent in the
first half of the 20th century. In the course of the
ensuing discussions about scientific discovery, the distinction
between the different features of scientific inquiry turned into a
powerful demarcation criterion. The boundary between context of
discovery (the de facto thinking processes) and context of
justification (the de jure defense of the correctness of
these thoughts) was now understood to determine the scope of
philosophy of science. The underlying assumption is that philosophy of
science is a normative endeavor. Advocates of the context distinction
argue that the generation of a new idea is an intuitive, irrational
process; it cannot be subject to normative analysis. Therefore, the
study of scientists' actual thinking can only be the subject of
psychology, sociology, and other empirical sciences. Philosophy of
science, by contrast, is exclusively concerned with the context of
justification.

The terms “context of discovery” and “context of
justification” are often associated with Hans Reichenbach's
work. Reichenbach's original conception of the context distinction is
quite complex, however (Howard 2006; Richardson 2006). It does not
map easily on to the disciplinary distinction mentioned above, because
for Reichenbach, philosophy of science proper is partly
descriptive. Reichenbach maintains that philosophy of science includes
a description of knowledge as it really is. Descriptive philosophy of
science reconstructs scientists' thinking processes in such a way that
logical analysis can be performed on them, and it thus prepares the
ground for the evaluation of these thoughts (Reichenbach 1938: §
1). Discovery, by contrast, is the object of
empirical—psychological, sociological—study. According to
Reichenbach, the empirical study of discoveries shows that processes
of discovery often correspond to the principle of induction, but this
is simply a psychological fact (Reichenbach 1938: 403).

While the terms “context of discovery” and “context
of justification” are widely used, there has been ample
discussion about how the distinction should be drawn and what their
philosophical significance is (c.f. Kordig 1978; Gutting 1980; Zahar
1983; Leplin 1987; Hoyningen-Huene 1987; Weber 2005: chapter 3;
Schickore and Steinle 2006). Most commonly, the distinction is
interpreted as a distinction between the process of conceiving a
theory and the validation of that theory, that is, the determination
of the theory's epistemic support. This version of the distinction is
not necessarily interpreted as a temporal distinction. In other words,
it is not usually assumed that a theory is first fully developed and
then validated. Rather, conception and validation are two different
epistemic approaches to theory: the endeavor to articulate, flesh out,
and develop its potential and the endeavor to assess its epistemic
worth. Within the framework of the context distinction, there are two
main ways of conceptualizing the process of conceiving a theory. The
first option is to characterize the generation of new knowledge as an
irrational act, a mysterious creative intuition, a “eureka
moment”. The second option is to conceptualize the generation of
new knowledge as an extended process that includes a creative
act as well as some process of articulating and developing the
creative idea.

Both of these accounts of knowledge generation served as starting
points for arguments against the possibility of a philosophy of
discovery. In line with the first option, philosophers have argued
that neither is it possible to prescribe a logical method that
produces new ideas nor is it possible to reconstruct logically the
process of discovery. Only the process of testing is amenable to
logical investigation. This objection to philosophies of discovery has
been called the “discovery machine objection” (Curd 1980:
207). It is usually associates with Karl Popper's Logic of
Scientific Discovery.

The initial state, the act of conceiving or inventing a theory, seems
to me neither to call for logical analysis not to be susceptible of
it. The question how it happens that a new idea occurs to a
man—whether it is a musical theme, a dramatic conflict, or a
scientific theory—may be of great interest to empirical
psychology; but it is irrelevant to the logical analysis of scientific
knowledge. This latter is concerned not with questions of
fact (Kant's quid facti?), but only with
questions of justification or validity (Kant's quid
juris?). Its questions are of the following kind. Can a
statement be justified? And if so, how? Is it testable? Is it
logically dependent on certain other statements? Or does it perhaps
contradict them? […]Accordingly I shall distinguish sharply
between the process of conceiving a new idea, and the methods and
results of examining it logically. As to the task of the logic of
knowledge—in contradistinction to the psychology of
knowledge—I shall proceed on the assumption that it consists
solely in investigating the methods employed in those systematic tests
to which every new idea must be subjected if it is to be seriously
entertained. (Popper 2002 [1934/1959]: 7-8)

With respect to the second way of conceptualizing knowledge
generation, many philosophers argue in a similar fashion that because
the process of discovery involves an irrational, intuitive process,
which cannot be examined logically, a logic of discovery cannot be
construed. Other philosophers turn against the philosophy of discovery
even though they explicitly acknowledge that discovery is an extended,
reasoned process. They present a meta-philosophical objection
argument, arguing that a theory of articulating and developing ideas
is not a philosophical but a psychological theory.

The impact of the context distinction on studies of scientific
discovery and on philosophy of science more generally can hardly be
overestimated. The view that the process of discovery (however
construed) is outside the scope of philosophy of science proper was
widely shared amongst philosophers of science for most of the
20th century and is still held by many. The last section
shows that there were a few attempts to develop logics of discovery in
the 1920s and 1930s. But for several decades, the context distinction
dictated what philosophy of science should be about and how it should
proceed. The dominant view was that theories of mental operations or
heuristics had no place in philosophy of science. Therefore, discovery
was not a legitimate topic for philosophy of science. The wide notion
of discovery is mostly deployed in sociological accounts of scientific
practice. In this perspective, “discovery” is understood
as a retrospective label, which is attributed as a sign of
accomplishment to some scientific endeavors. Sociological theories
acknowledge that discovery is a collective achievement and the outcome
of a process of negotiation through which “discovery
stories” are constructed and certain knowledge claims are
granted discovery status (Brannigan 1981; Schaffer 1986, 1994). Until
the last third of the 20th century, there were few attempts
to challenge the disciplinary distinction tied to the context
distinction. Only in the 1970s did the interest in philosophical
approaches to discovery begin to increase. But the context distinction
remained a challenge for philosophies of discovery.

There are three main lines of response to the disciplinary distinction
tied to the context distinction. Each of these lines of response opens
up a philosophical perspective on discovery. Each proceeds on the
assumption that philosophy of science may legitimately include some
form of analysis of actual reasoning patterns as well as
information from empirical sciences such as cognitive science,
psychology, and sociology. All of these responses reject the idea that
discovery is nothing but a mystical event. Discovery is conceived as
an analyzable reasoning process, not just as a creative leap by which
novel ideas spring into being fully formed. All of these responses
agree that the procedures and methods for arriving at new hypotheses
and ideas are no guarantee that the hypothesis or idea that is thus
formed is necessarily the best or the correct one. Nonetheless, it is
the task of philosophy of science to provide rules for making this
process better. All of these responses can be described as theories of
problem solving, whose ultimate goal is to make the generation of new
ideas and theories more efficient.

But the different approaches to scientific discovery employ different
terminologies. In particular, the term “logic” of
discovery is sometimes used in a narrow sense and sometimes broadly
understood. In the narrow sense, “logic” of discovery is
understood to refer to a set of formal, generally applicable rules by
which novel ideas can be mechanically derived from existing data. In
the broad sense, “logic” of discovery refers to the
schematic representation of reasoning
procedures. “Logical” is just another term for
“rational”. Moreover, while each of these responses
combines philosophical analyses of scientific discovery with empirical
research on actual human cognition, different sets of resources are
mobilized, ranging from AI research and cognitive science to
historical studies of problem-solving procedures. Also, the responses
parse the process of scientific inquiry differently. Often, scientific
inquiry is regarded as having two aspects, viz. generation and
validation of new ideas. At times, however, scientific inquiry is
regarded as having three aspects, namely generation, pursuit or
articulation, and validation of knowledge. In the latter framework,
the label “discovery” is sometimes used to refer just to
generation and sometimes to refer to both generation and
pursuit.

The first response to the challenge of the context distinction draws
on a broad understanding of the term “logic” to argue that
a logic of scientific discovery can be developed
(section 6). The second response,
drawing on a narrow understanding of the term “logic”, is
to concede that there is no logic of discovery, i.e., no
algorithm for generating new knowledge. Philosophers who take this
approach argue that the process of discovery follows an identifiable,
analyzable pattern (section 7). Others argue
that discovery is governed by a methodology. The methodology
of discovery is a legitimate topic for philosophical analysis
(section 8). All of these responses assume that
there is more to discovery than a “eureka moment.”
Discovery comprises processes of articulating and developing the
creative thought. These are the processes that can be examined with
the tools of philosophical analysis. The third response to the
challenge of the context distinction also assumes that discovery is or
at least involves a creative act. But in contrast to the first two
responses, it is concerned with the creative act itself. Philosophers
who take this approach argue that scientific creativity is amenable to
philosophical analysis (section 9).

The first response to the challenge of the context distinction is to
argue that discovery is a topic for philosophy of science because it
is a logical process after all. Advocates of this approach to the
logic of discovery usually accept the overall distinction between the
two processes of conceiving and testing a hypothesis. They also agree
that it is impossible to put together a manual that provides a formal,
mechanical procedure through which innovative concepts or hypotheses
can be derived: There is no discovery machine. But they reject the
view that the process of conceiving a theory is a creative act, a
mysterious guess, a hunch, a more or less instantaneous and random
process. Instead, they insist that both conceiving and testing
hypotheses are processes of reasoning and systematic inference, that
both of these processes can be represented schematically, and that it
is possible to distinguish better and worse paths to new
knowledge.

This line of argument has much in common with the logics of discovery
described in section 4 above but it is now
explicitly pitched against the disciplinary distinction tied to the
context distinction. There are two main ways of developing this
argument. The first is to conceive of discovery in terms of abductive
reasoning (section 6.1). The second is to
conceive of discovery in terms of problem-solving algorithms, whereby
heuristic rules aid the processing of available data and enhance the
success in finding solutions to problems
(section 6.2). Both lines of argument rely
on a broad conception of logic,
whereby the “logic” of discovery amounts to a schematic
account of the reasoning processes involved in knowledge
generation.

One argument, elaborated prominently by Norwood R. Hanson, is that the
act of discovery—here, the act of suggesting a new
hypothesis—follows a distinctive logical pattern, which is
different from both inductive logic and the logic of
hypothetico-deductive reasoning. The special logic of discovery is the
logic of abductive or “retroductive” inferences (Hanson
1958). The argument that it is through an act of abductive inferences
that plausible, promising scientific hypotheses are devised goes back
to C.S. Peirce. This version of the logic of discovery characterizes
reasoning processes that take place before a new hypothesis
is ultimately justified. The abductive mode of reasoning that leads to
plausible hypotheses is conceptualized as an inference beginning with
data or, more specifically, with surprising or anomalous
phenomena.

In this view, discovery is primarily a process of explaining anomalies
or surprising, astonishing phenomena. The scientists' reasoning
proceeds abductively from an anomaly to an explanatory hypothesis in
light of which the phenomena would no longer be surprising or
anomalous. The outcome of this reasoning process is not one single
specific hypothesis but the delineation of a type of hypotheses that
is worthy of further attention (Hanson 1965: 64). According to
Hanson, the abductive argument has the following schematic form
(Hanson 1960: 104):

Some surprising, astonishing phenomena p1,
p2, p3 … are encountered.

But p1, p2, p3 …
would not be surprising were an hypothesis of H's type to obtain. They
would follow as a matter of course from something like H and would be
explained by it.

Therefore there is good reason for elaborating an hypothesis of
type H—for proposing it as a possible hypothesis from whose
assumption p1, p2, p3
… might be explained.

Drawing on the historical record, Hanson argues that several important
discoveries were made relying on abductive reasoning, such as Kepler's
discovery of the elliptic orbit of Mars (Hanson 1958). It is now
widely agreed, however, that Hanson's reconstruction of the episode is
not a historically adequate account of Kepler's discovery (Lugg
1985). More importantly, while there is general agreement that
abductive inferences are frequent in both everyday and scientific
reasoning, these inferences are no longer considered
as logical inferences. Even if one accepts Hanson's schematic
representation of the process of identifying plausible hypotheses,
this process is a “logical” process only in the widest
sense whereby the term “logical” is understood as
synonymous with “rational”. Notably, some philosophers
have even questioned the rationality of abductive inferences (Koehler
1991; Brem and Rips 2000).

Another argument against the above schema is that it is too
permissive. There will be several hypotheses that are explanations for
phenomena p1, p2, p3
…, so the fact that a particular hypothesis explains the
phenomena is not a decisive criterion for developing that hypothesis
(Harman 1965; see also Blackwell 1969). Additional criteria are
required to evaluate the hypothesis yielded by abductive
inferences.

Finally, it is worth noting that the schema of abductive reasoning
does not explain the very act of conceiving a hypothesis or
hypothesis-type. The processes by which a new idea is first
articulated remain unanalyzed in the above schema. The schema focuses
on the reasoning processes by which an exploratory hypothesis is
assessed in terms of its merits and promise (Laudan 1980; Schaffner
1993).

In more recent work on abduction and discovery, two notions of
abduction are sometimes distinguished: the common notion of abduction
as inference to the best explanation (selective abduction) and
creative abduction (Magnani 2000, 2009). Selective abduction—the
inference to the best explanation—involves selecting a
hypothesis from a set of known hypotheses. Medical diagnosis
exemplifies this kind of abduction. Creative abduction, by contrast,
involves generating a new, plausible hypothesis. This happens, for
instance, in medical research, when the notion of a new disease is
articulated. However, it is still an open question whether this
distinction can be drawn, or whether there is a more gradual
transition from selecting an explanatory hypothesis from a familiar
domain (selective abduction) to selecting a hypothesis that is
slightly modified from the familiar set and to identifying a more
drastically modified or altered assumption.

Another recent suggestion is to broaden Peirce's original account of
abduction and to include not only verbal information but also
non-verbal mental representations, such as visual, auditory, or motor
representations. In Thagard's approach, representations are
characterized as patterns of activity in mental populations (see
also section 9.3 below). The advantage of the
neural account of human reasoning is that it covers features such as
the surprise that accompanies the generation of new insights or the
visual and auditory representations that contribute to it. If all
mental representations can be characterized as patterns of firing in
neural populations, abduction can be analyzed as the combination or
“convolution” (Thagard) of patterns of neural activity
from disjoint or overlapping patterns of activity (Thagard 2010).

The concern with the logic of discovery has also motivated research on
artificial intelligence at the intersection of philosophy of science
and cognitive science. In this approach, scientific discovery is
treated as a form of problem-solving activity (Simon 1973; see also
Newell and Simon 1971), whereby the systematic aspects of problem
solving are studied within an information-processing framework. The
aim is to clarify with the help of computational tools the nature of
the methods used to discover scientific hypotheses. These hypotheses
are regarded as solutions to problems. Philosophers working in this
tradition build computer programs employing methods of heuristic
selective search (e.g., Langley et al. 1987). In computational
heuristics, search programs can be described as searches for solutions
in a so-called “problem space” in a certain domain. The
problem space comprises all possible configurations in that domain
(e.g., for chess problems, all possible arrangements of pieces on a
board of chess). Each configuration is a “state” of the
problem space. There are two special states, namely the goal state,
i.e., the state to be reached, and the initial state, i.e., the
configuration at the starting point from which the search
begins. There are operators, which determine the moves that generate
new states from the current state. There are path constraints, which
limit the permitted moves. Problem solving is the process of searching
for a solution of the problem of how to generate the goal state from
an initial state. In principle, all states can be generated by
applying the operators to the initial state, then to the resulting
state, until the goal state is reached (Langley et al. 1987: chapter
9). A problem solution is a sequence of operations leading from the
initial to the goal state.

The basic idea behind computational heuristics is that rules can be
identified that serve as guidelines for finding a solution to a given
problem quickly and efficiently by avoiding undesired states of the
problem space. These rules are best described as rules of thumb. The
aim of constructing a logic of discovery thus becomes the aim of
constructing a heuristics for the efficient search for solutions to
problems. The term “heuristic search” indicates that in
contrast to algorithms, problem-solving procedures lead to results
that are merely provisional and plausible. A solution is not
guaranteed, but heuristic searches are advantageous because they are
more efficient than exhaustive random trial and error
searches. Insofar as it is possible to evaluate whether one set of
heuristics is better—more efficacious—than another, the
logic of discovery turns into a normative theory of discovery.

Arguably, because it is possible to reconstruct important scientific
discovery processes with sets of computational heuristics, the
scientific discovery process can be considered as a special case of
the general mechanism of information processing. In this context, the
term “logic” is not used in the narrow sense of a set of
formal, generally applicable rules to draw inferences but again in a
broad sense as a label for a set of procedural rules.

The computer programs that embody the principles of heuristic searches
in scientific inquiry simulate the paths that scientists followed when
they searched for new theoretical hypotheses. Computer programs such
as BACON (Simon et al. 1981) and KEKADA (Kulkarni and Simon 1988)
utilize sets of problem-solving heuristics to detect regularities in
given data sets. The program would note, for instance, that the values
of a dependent term are constant or that a set of values for a
term x and a set of values for a term y are linearly
related. It would thus “infer” that the dependent term
always has that value or that a linear relation exists
between x and y. These programs can “make
discoveries” in the sense that they can simulate successful
discoveries such as Kepler's third law (BACON) or the Krebs cycle
(KEKADA).

AI-based theories of scientific discoveries have helped identify and
clarify a number of problem-solving strategies. An example of such a
strategy is heuristic means-ends analysis, which involves identifying
specific differences between the present and the goal situation and
searches for operators (processes that will change the situation) that
are associated with the differences that were detected. Another
important heuristic is to divide the problem into sub-problems and to
begin solving the one with the smallest number of unknowns to be
determined (Simon 1977). AI-based approaches have also highlighted the
extent to which the generation of new knowledge draws on existing
knowledge that constrains the development of new hypotheses.

As accounts of scientific discoveries, computational heuristics have
some limitations. Most importantly, because computer programs require
the data from actual experiments the simulations cover only certain
aspects of scientific discoveries. They do not design new experiments,
instruments, or methods. Moreover, compared to the problem spaces
given in computational heuristics, the complex problem spaces for
scientific problems are often ill defined, and the relevant search
space and goal state must be delineated before heuristic assumptions
could be formulated (Bechtel and Richardson 1993: chapter 1).

Earlier critics of AI-based theories of scientific discoveries
argued that a computer cannot devise new concepts but is confined to
the concepts included in the given computer language (Hempel 1985:
119–120). Subsequent work has shown that computational methods can be
used to generate new results leading to refereed scientific
publications in astronomy, cancer research, ecology, and other fields
(Langley 2000). The most recent computational research on scientific
discovery is no longer driven by philosophical interests in scientific
discovery, however. Instead, the main motivation is to contribute
computational tools to aid scientists in their research.

Many philosophers maintain that discovery is a legitimate topic for
philosophy of science while abandoning the notion that there is a
logic of discovery. One very influential approach is Thomas
Kuhn's analysis of the emergence of novel facts and theories
(Kuhn 1970 [1962]: chapter 6). Kuhn identifies a general pattern of discovery
as part of his account of scientific change. A discovery is not a
simple act, but an extended, complex process, which culminates in
paradigm changes. Paradigms are the symbolic generalizations,
metaphysical commitments, values, and exemplars that are shared by a
community of scientists and that guide the research of that community.
Paradigm-based, normal science does not aim at novelty but instead at
the development, extension, and articulation of accepted paradigms. A
discovery begins with an anomaly, that is, with the recognition that
the expectations induced by an established paradigm are being violated.
The process of discovery involves several aspects: observations of an
anomalous phenomenon, attempts to conceptualize it, and changes in the
paradigm so that the anomaly can be accommodated.

It is the mark of success of normal science that it does not make
transformative discoveries, and yet such discoveries come about as a
consequence of normal, paradigm-guided science. The more detailed and
the better developed a paradigm, the more precise are its predictions.
The more precisely the researchers know what to expect, the better
they are able to recognize anomalous results and violations of
expectations:

novelty ordinarily emerges only for the man who,
knowing with precision what he should expect, is able to recognize
that something has gone wrong. Anomaly appears only against the
background provided by the paradigm. (Kuhn 1970 [1962]:
65)

Drawing on several historical examples, Kuhn argues that it is usually
impossible to identify the very moment when something was discovered
or even the individual who made the discovery. Kuhn illustrates these
points with the discovery of oxygen (see Kuhn 1970 [1962]:
53–56). Oxygen had not been discovered before 1774 and had been
discovered by 1777. Even before 1774, Lavoisier had noticed that
something was wrong with phlogiston theory, but he was unable to move
forward. Two other investigators, C. W. Scheele and Joseph Priestley,
independently identified a gas obtained from heating solid substances.
But Scheele's work remained unpublished until after 1777, and
Priestley did not identify his substance as a new sort of gas. In
1777, Lavoisier presented the oxygen theory of combustion, which gave
rise to fundamental reconceptualization of chemistry. But according to
this theory as Lavoisier first presented it, oxygen was not a chemical
element. It was an atomic “principle of acidity” and
oxygen gas was a combination of that principle with caloric. According
to Kuhn, all of these developments are part of the discovery of
oxygen, but none of them can be singled out as “the” act
of discovery.

In pre-paradigmatic periods or in times of paradigm crisis,
theory-induced discoveries may happen. In these periods, scientists
speculate and develop tentative theories, which may lead to novel
expectations and experiments and observations to test whether these
expectations can be confirmed. Even though no precise predictions can
be made, phenomena that are thus uncovered are often not quite what
had been expected. In these situations, the simultaneous exploration
of the new phenomena and articulation of the tentative hypotheses
together bring about discovery.

In cases like the discovery of oxygen, by contrast, which took place
while a paradigm was already in place, the unexpected becomes apparent
only slowly, with difficulty, and against some resistance. Only
gradually do the anomalies become visible as such. It takes time for
the investigators to recognize “both that something is and what
it is” (Kuhn 1970 [1962]: 55). Eventually, a new paradigm
becomes established and the anomalous phenomena become the expected
phenomena.

Recent studies in cognitive neuroscience of brain activity during
periods of conceptual change support Kuhn's view that conceptual
change is hard to achieve. These studies examine the neural processes
that are involved in the recognition of anomalies and compare them
with the brain activity involved in the processing of information that
is consistent with preferred theories. The studies suggest that the
two types of data are processed differently (Dunbar et al. 2007).

Advocates of the view that there are methodologies of discovery use
the term “logic” in the narrow sense of an algorithmic
procedure to generate new ideas. But like the AI-based theories of
scientific discovery described in
section 6, methodologies of
scientific discovery interpret the concept
“discovery” as a label for an extended process of
generating and articulating new ideas and often describe the process
in terms of problem solving. In these approaches, the distinction
between the contexts of discovery and the context of justification is
challenged because the methodology of discovery is understood to play
a justificatory role. Advocates of a methodology of discovery usually
rely on a distinction between different justification procedures,
justification involved in the process of generating new knowledge and
justification involved in testing it. Consequential or
“strong” justifications are methods of testing. The
justification involved in discovery, by contrast, is conceived as
generative (as opposed to consequential) justification
(section 8.1) or as weak (as opposed to strong)
justification (section 8.2). Again, some
terminological ambiguity exists because according to some
philosophers, there are three contexts, not two: Only the initial
conception of a new idea (the “eureka moment”) is the
context of discovery proper, and between it and justification there
exists a separate context of pursuit (Laudan 1980). But many advocates
of methodologies of discovery regard the context of pursuit as an
integral part of the process of justification. They retain the notion
of two contexts and re-draw the boundaries between the contexts of
discovery and justification as they were drawn in the early
20th century.

The methodology of discovery has sometimes been characterized as a
form of justification that is complementary to the methodology of
testing (Nickles 1984, 1985, 1989). According to the methodology of
testing, empirical support for a theory results from successfully
testing the predictive consequences derived from that theory (and
appropriate auxiliary assumptions). In light of this methodology,
justification for a theory is “consequential
justification,” the notion that a hypothesis is established if
successful novel predictions are derived from the theory or
claim. Generative justification complements consequential
justification. Advocates of generative justification hold that there
exists an important form of justification in science that involves
reasoning to a claim from data or previously established
results more generally.

One classic example for a generative methodology is the set of
Newton's rules for the study of natural philosophy. According to these
rules, general propositions are established by deducing them from the
phenomena. The notion of generative justification seeks to preserve
the intuition behind classic conceptions of justification by
deduction. Generative justification amounts to the rational
reconstruction of the discovery path in order to establish its
discoverability had the researchers known what is known now,
regardless of how it was first thought of (Nickles 1985, 1989). The
reconstruction demonstrates in hindsight that the claim could have
been discovered in this manner had the necessary information and
techniques been available. In other words, generative
justification—justification as “discoverability” or
“potential discovery”—justifies a knowledge claim by
deriving it from results that are already established. While
generative justification does not retrace exactly those steps of the
actual discovery path that were actually taken, it is a better
representation of scientists' actual practices than consequential
justification because scientists tend to construe new claims from
available knowledge. Generative justification is a weaker version of
the traditional ideal of justification by deduction from the
phenomena. Justification by deduction from the phenomena is complete
if a theory or claim is completely determined from what we already
know. The demonstration of discoverability results from the successful
derivation of a claim or theory from the most basic and most solidly
established empirical information.

Discoverability as described in the previous paragraphs is a mode of
justification. Like the testing of novel predictions derived from a
hypothesis, generative justification begins when the phase of finding
and articulating a hypothesis worthy of assessing is drawing to a
close. Other approaches to the methodology of discovery are directly
concerned with the procedures involved in devising new hypotheses. The
argument in favor of this kind of methodology is that the procedures
of devising new hypotheses already include elements of
appraisal. These preliminary assessments have been termed
“weak” evaluation procedures (Schaffner 1993). Weak
evaluations are relevant during the process of devising a new
hypothesis. They provide reasons for accepting a hypothesis as
promising and worthy of further attention. Strong evaluations, by
contrast, provide reasons for accepting a hypothesis as
(approximately) true or confirmed. Both “generative” and
“consequential” testing as discussed in the previous
section are strong evaluation procedures. Strong evaluation
procedures are rigorous and systematically organized according to the
principles of hypothesis derivation or H-D testing. A methodology of
preliminary appraisal, by contrast, articulates criteria for the
evaluation of a hypothesis prior to rigorous derivation or testing. It
aids the decision about whether to take that hypothesis seriously
enough to develop it further and test it. For advocates of this
version of the methodology of discovery, it is the task of philosophy
of science to characterize sets of constraints and methodological
rules guiding the complex process of prior-to-test evaluation of
hypotheses.

In contrast to the computational approaches discussed above,
strategies of preliminary appraisal are not regarded as
subject-neutral but as specific to particular fields of study. Because
the analysis of criteria for the appraisal of hypotheses has mostly
been made with regard to the study of biological mechanism, the
criteria and constraints that have been proposed are those that play a
role in the discovery of biological mechanisms. Biological mechanisms
are entities and activities that are organized in such a way that they
produce regular changes from initial to terminal conditions (Machamer
et al. 2000).

Philosophers of biology have developed a fine-grained framework to
account for the generation and preliminary evaluation of these
mechanisms (Darden 2002; Craver 2002; Bechtel and Richardson 1993;
Craver and Darden 2013). Some philosophers have even suggested that
the phase of preliminary appraisal be further divided into two phases,
the phase of appraising and the phase of revising. According to
Lindley Darden, the phases of generation, appraisal and revision of
descriptions of mechanisms can be characterized as reasoning processes
governed by reasoning strategies. Different reasoning strategies
govern the different phases (Darden 1991, 2002; Craver 2002; Darden
2009). The generation of hypotheses about mechanisms, for instance, is
governed by the strategy of “schema instantiation” (see
Darden 2002). The discovery of the mechanism of protein synthesis
involved the instantiation of an abstract schema for chemical
reactions: reactant1 + reactant2 = product. The
actual mechanism of protein synthesis was found through specification
and modification of this schema.

It is important to appreciate the status of these reasoning
strategies. They are not necessarily strategies that were actually
used. Neither of these strategies is deemed necessary for discovery,
and they are not prescriptions for biological research. Rather, these
strategies are deemed sufficient for the discovery of mechanisms; they
“could have been used” to arrive at the description of
that mechanism (Darden 2002). The methodology of the discovery of
mechanisms is an extrapolation from past episodes of research on
mechanisms and the result of a synthesis of rational reconstructions
of several of these historical episodes. The methodology of discovery
is only weakly normative in the sense that the strategies for the
discovery of mechanisms that have been identified so far may prove
useful in future biological research. Moreover, the sets of reasoning
strategies that have been proposed are highly specific. It is still an
open question whether the analysis of strategies for the discovery of
biological mechanisms can illuminate the efficiency of scientific
problem solving more generally (Weber 2005: chapter 3).

The approaches to scientific discovery presented in the previous
sections focus on the adoption, articulation, and preliminary
evaluation of ideas or hypotheses prior to rigorous testing. They do
not illuminate how a novel hypothesis or idea is first thought
up. Even among philosophers of discovery, the predominant view has
long been that there is an initial step of discovery that is best
described as a “eureka moment”, a mysterious intuitive
leap of the human mind that cannot be analyzed further.

The concept of discovery as hypothesis-formation as it is encapsulated
in the traditional distinction between context of discovery and
context of justification does not explicate how new ideas are
formed. According to accounts of discovery informed by evolutionary
biology, the generation of new ideas is akin to random, blind
variations of thought processes, which have to be inspected by the
critical mind and assessed as neutral, productive, or useless
(Campbell 1960; see also Hull 1988). While the evolutionary approach
to discovery offers a more substantial account of scientific
discovery, the key processes by which random ideas are generated are
still left unanalyzed.

Today, many philosophers hold the view that creativity is not
mysterious and can be submitted to analysis. Margaret Boden has
offered helpful analyses of the concept of creativity. According to
Boden, a new development is creative if it is novel, surprising, and
important. She distinguishes between psychological creativity
(P-creativity) and historical creativity (H-creativity). P-creativity
is a development that is new, surprising and important to the
particular person who comes up with it. H-creativity, by contrast, is
radically novel, surprising, and important—it is generated for
the first time (Boden 2004).

The majority of recent philosophical studies of scientific discovery
today focus on the act of generation of new knowledge. The distinctive
feature of these studies is that they integrate approaches from
cognitive science, psychology, and computational neuroscience (Thagard
2012). Recent work on creativity offers substantive analyses of the
social and psychological preconditions and the cognitive mechanisms
involved in generating new ideas. Some of this research aims to
characterize those features that are common to all creative processes.
Other research aims to identify the features that are distinctive of
scientific creativity (as opposed to other forms of creativity, such
as artistic creativity or creative technological invention). Studies
have focused on analyses of the personality traits that are conducive
to creative thinking, and the social and environmental factors that
are favorable for discovery
(section 9.1). Two key elements of the
cognitive processes involved in
creative thinking are analogies (section 9.2) and
mental models (section 9.3).

Psychological studies of creative individuals' behavioral dispositions
suggest that creative scientists share certain personality traits,
including confidence, openness, dominance, independence, introversion,
as well as arrogance and hostility. (For overviews of recent studies
on personality traits of creative scientists, see Feist 1999, 2006:
chapter 5). Social situatedness has also been explored as an important
resource for creativity. In this perspective, the sociocultural
structures and practices in which individuals are embedded are
considered crucial for the generation of creative ideas. Both
approaches suggest that creative individuals usually have outsider
status—they are socially deviant and diverge from the
mainstream.

Outsider status is also a key feature of standpoint. According to
standpoint theorists, people with standpoint are politically aware and
politically engaged people outside the mainstream. Some standpoint
theorists suggest exploiting this similarity for creativity research.
Because people with standpoint have different experiences and access
to different domains of expertise than most members of a culture, they
can draw on rich conceptual resources for creative
thinking. Standpoint theory may thus be an important resource for the
development of social and environmental approaches to the study of
creativity (Solomon 2007).

Many philosophers of science highlight the role of analogy in the
development of new knowledge, whereby analogy is understood as a
process of bringing ideas that are well understood in one domain to
bear on a new domain (Thagard 1984; Holyoak and Thagard 1996). An
important source for philosophical thought about analogy is Mary
Hesse's conception of models and analogies in theory construction and
development. In this approach, analogies are similarities between
different domains. Hesse introduces the distinction between positive,
negative, and neutral analogies (Hesse 1966: 8). If we consider the
relation between gas molecules and a model for gas, namely a
collection of billiard balls in random motion, we will find properties
that are common to both domains (positive analogy) as well as
properties that can only be ascribed to the model but not to the
target domain (negative analogy). There is a positive analogy between
gas molecules and a collection of billiard balls because both the
balls and the molecules move randomly. There is a negative analogy
between the domains because billiard balls are colored, hard, and
shiny but gas molecules do not have these properties. The most
interesting properties are those properties of the model about which
we do not know whether they are positive or negative analogies. This
set of properties is the neutral analogy. These properties are the
significant properties because they might lead to new insights about
the less familiar domain. From our knowledge about the familiar
billiard balls, we may be able to derive new predictions about the
behavior of gas molecules, which we could then test.

Hesse offers a more detailed analysis of the structure of analogical
reasoning through the distinction between horizontal and vertical
analogies between domains. Horizontal analogies between two domains
concern the sameness or similarity between properties of both domains.
If we consider sound and light waves, there are similarities between
them: sound echoes, light reflects; sound is loud, light is bright,
both sound and light are detectable by our senses. There are also
relations among the properties within one domain, such as the causal
relation between sound and the loud tone we hear and, analogously,
between physical light and the bright light we see. These analogies
are vertical analogies. For Hesse, vertical analogies hold the key for
the construction of new theories.

Analogies play several roles in science. Not only do they contribute
to discovery but they also play a role in the development and
evaluation of scientific theories. Current discussions about analogy
and discovery have expanded and refined Hesse's approach in various
ways. Some philosophers have developed criteria for evaluating analogy
arguments (Bartha 2010). Other work has identified highly significant
analogies that were particularly fruitful for the advancement of
science (Holyoak and Thagard 1996: 186–188; Thagard 1999:
chapter 9). The majority of analysts explore the features of the
cognitive mechanisms through which aspects of a familiar domain or
source are applied to an unknown target domain in order to understand
what is unknown. According to the influential multi-constraint theory
of analogical reasoning developed by Holyoak and Thagard, the transfer
processes involved in analogical reasoning (scientific and otherwise)
are guided or constrained in three main ways: 1) by the direct
similarity between the elements involved; 2) by the structural
parallels between source and target domain; as well as 3) by the
purposes of the investigators, i.e., the reasons why the analogy is
considered. Discovery, the formulation of a new hypothesis, is one
such purpose.

“In vivo” investigations of scientists reasoning in their
laboratories have not only shown that analogical reasoning is a key
component of scientific practice, but also that the distance between
source and target depends on the purpose for which analogies are
sought. Scientists trying to fix experimental problems draw analogies
between targets and sources from highly similar domains. In contrast,
scientists attempting to formulate new models or concepts draw
analogies between less similar domains. Analogies between radically
different domains, however, are rare (Dunbar 1997, 2001).

In current cognitive science, human cognition is often explored in
terms of model-based reasoning. The starting point of this approach is
the notion that much of human reasoning, including probabilistic and
causal reasoning as well as problem solving takes place through mental
modeling rather than through the application of logic or
methodological criteria to a set of propositions (Johnson-Laird 1983;
Magnani et al. 1999; Magnani and Nersessian 2002). In model-based
reasoning, the mind constructs a structural representation of a
real-world or imaginary situation and manipulates this structure. In
this perspective, conceptual structures are viewed as models and
conceptual innovation as constructing new models through various
modeling operations. Analogical reasoning—analogical
modeling—is regarded as one of three main forms of model-based
reasoning that appear to be relevant for conceptual innovation in
science. Besides analogical modeling, visual modeling and simulative
modeling or thought experiments also play key roles (Nersessian 1992,
1999, 2009). These modeling practices are constructive in that they
aid the development of novel mental models. The key elements of
model-based reasoning are the call on knowledge of generative
principles and constraints for physical models in a source domain and
the use of various forms of abstraction. Conceptual innovation results
from the creation of new concepts through processes that abstract and
integrate source and target domains into new models (Nersessian
2009).

Some critics have argued that despite the large amount of work on the
topic, the notion of mental model is not sufficiently clear. Thagard
seeks to clarify the concept by characterizing mental models in terms
of neural processes (Thagard 2010). In his approach, mental models are
produced through complex patterns of neural firing, whereby the
neurons and the interconnections between them are dynamic and
changing. A pattern of firing neurons is a representation when there
is a stable causal correlation between the pattern or activation and
the thing that is represented. In this research, questions about the
nature of model-based reasoning are transformed into questions about
the brain mechanisms that produce mental representations.

The above sections show that the study of scientific discovery has
become an integral part of the wider endeavor of exploring creative
thinking and creativity more generally. Naturalistic philosophical
approaches combine conceptual analysis of processes of knowledge
generation with empirical work on creativity, drawing heavily and
explicitly on current research in psychology and cognitive science,
and on in vivo laboratory observations, and, most recently,
on brain imaging techniques.